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High-resolution Spiral First-pass Myocardial Perfusion Imaging using DEep learning-based rapid Spiral Image REconstruction (DESIRE)
Junyu Wang1, Daniel Weller2, Patricia Rodriguez Lozano3, and Michael Salerno1,3,4
1Biomedical Engineering, University of Virginia, Charlottesville, VA, United States, 2Electrical and Computer Engineering, University of Virginia, Charlottesville, VA, United States, 3Medicine, University of Virginia, Charlottesville, VA, United States, 4Radiology, University of Virginia, Charlottesville, VA, United States

Synopsis

First-pass contrast-enhanced myocardial perfusion imaging is valuable for evaluating coronary artery disease (CAD). Spiral perfusion imaging techniques, using a motion-compensated L1-SPIRiT based reconstruction, are capable of whole-heart high-resolution perfusion imaging. However, this reconstruction is performed off-line and takes ~1 hour per slice. To address this limitation, we developed a DEep learning-based Spiral Image REconstruction technique (DESIRE) for spiral first-pass myocardial perfusion imaging, for both single-slice (SS) and simultaneous multi-slice (SMS) MB=2 acquisitions, to provide fast and high-quality image reconstruction and make rapid online reconstruction feasible. High image quality was demonstrated using the proposed technique for healthy volunteers and patients.

Introduction

First-pass contrast-enhanced cardiac magnetic resonance (CMR) perfusion imaging, which is non-invasive and non-radioactive, has proven to be a valuable tool for evaluating patients with CAD1–4. Recently, we have developed spiral single-slice (SS) and simultaneous multi-slice (SMS) perfusion pulse sequence and the motion-compensated (SMS-Slice)-L1-SPIRiT reconstruction technique capable of whole-heart high-resolution perfusion imaging5–8. However, this compressed-sensing based image reconstruction technique is time-consuming and takes ~1 hour per slice, hence it can’t provide immediate feedback to doctors and impedes clinical translation. The goal of this study is to develop and evaluate a DEep learning-based Spiral Image REconstruction technique (DESIRE) for spiral first-pass myocardial perfusion imaging, for both SS and SMS MB=2 acquisitions, to provide fast and high-quality image reconstruction (Figure 1 (a)).

Methods

Data Acquisition and Preprocessing SS and SMS golden-angle spiral perfusion data sets with 1.25×1.25 mm2 in-plane spatial resolution and whole-heart coverage (6-8 slices) were previously acquired from 18 healthy volunteers and 4 patients undergoing clinical studies on 3 T SIEMENS Skyra/Prisma scanners8,9. The in-plane acceleration factor for SS and SMS MB=2 was approximately 10 and 5, respectively.

Before feeding the data to the network, coil-selection10, motion-correction11, and adaptive phase combination12 were performed on the NUFFT-gridded13 multi-coil image series at each slice location. Images were cropped into (Frames) to save memory. Each dynamic image series were normalized to 0-1.

Specifically, for SMS MB=2 acquisitions, to prevent slice-leakage artifact from being learned by the SMS network, the ground-truth data were SS L1-SPIRiT images from two slice locations, and the SMS network input images were the retrospective data from the two separate images with Hadamard SMS MB=2 phase modulation (Figure 1 (c))14.

156 slices from 20 subjects were used for training, and another 14 slices from 2 subjects were used for validation. Another 56 slices from 8 subjects with SS and 76 slices from 10 subjects with SMS MB=2 acquisitions were used for testing the performance.

Image Reconstruction Network Figure 1 (b) illustrates the proposed 3D U-Net15 based image reconstruction network. The inputs to the network were complex-valued under-sampled single-channel perfusion image series from a given slice location after coil-selection10, motion-correction11, and adaptive phase combination12. The real and imaginary values were concatenated into two channels. The outputs were the concatenated real and imaginary perfusion image series. SS and SMS network were trained separately where L1-SPIRiT reconstruction results served as the ground truth.

To compare networks, we set the network with and without complex convolution as the baseline. The performance of the network with respect to the number of kernels (Ninit) at initial layer and the depth of the network (D) was explored (Table 1). Furthermore, we sought to evaluate complex convolutional networks16 to preserve the phase information in the raw data to see if this could further improve reconstruction quality.

Experimental Setup The training of the baseline network was conducted using PyTorch on a single NVIDIA Tesla P100 GPU (12 GB memory). The training of the other networks was conducted on four P100 GPUs due to the memory limitation of a single GPU. All of the trainings were conducted for 150 epochs with an L1 loss (absolute error) and a batch size of 4. The shortest training time was baseline network which took ~8 hours, while the longest training time was complex convolution network which took ~20 hours. All of the testing experiments were conducted on a single P100 GPU.

Image Analysis Both structural similarity index (SSIM)17 and root mean square error (RMSE) for the SS and respective SMS MB=2 reconstructed using DEIRE were assessed with respect to the ground truth. Prospective SS and SMS images were graded by an experienced cardiologist (5, excellent; 1, poor).

Results

Figure 2 and 3 show examples from the test data for the SS (Figure 2) and SMS MB=2 (Figure 3) reconstructions using the proposed DESIRE technique. Good image quality was demonstrated.

Figure 4 shows an example case using SS acquisition undergoing clinical stress spiral perfusion imaging. Good image quality and temporal fidelity with respect to the ground truth were demonstrated.

Table 1 (a) and (b) show image quality scores for both SS and SMS MB=2 with different network structures. Table 1 (c) shows the corresponding reconstruction time per slice of the test data on a NVIDIA Tesla P100 GPU, while the reconstruction time of using L1-SPIRiT with 80 iterations on an Intel Xeon CPU (2.40 GHz) was ~1 hour per slice. Increasing the depth and the number of initial kernels help improve the reconstruction performance. However, for SS with Ninit=32 , the performance of D=2 is similar to D=3, which indicates the max capacity of the reconstruction network is reached. Particularly, for the baseline network with complex convolution operations, the performance is improved. Scores from cardiologist showed a preference to networks with more initial kernels and those using complex convolutions, which was consistent with the SSIM and RMSE quantification.

Discussion and Conclusion

The proposed image reconstruction network (DESIRE) enabled rapid and high-quality image reconstruction for both SS and SMS MB=2 whole-heart ultra-high resolution first-pass spiral perfusion imaging. Further optimization of the SMS reconstruction network, such as incorporating the through-plane kernels is still required for optimal performance.

Acknowledgements

This work was supported by NIH R01 HL131919 and Wallace H. Coulter Foundation Grant.

References

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6. Yang Y, Meyer CH, Epstein FH, Kramer CM, Salerno M. Whole‐heart spiral simultaneous multi‐slice first‐pass myocardial perfusion imaging. Magnetic Resonance in Medicine 2019;81:852–862.

7. Wang J, Yang Y, Zhou R, Jacob M, Weller DS, Salerno M. SMS Slice L1-SPIRiT: auto-calibrated image reconstruction for spiral simultaneous multi-slice first-pass perfusion imaging with 1.25 mm resolution and whole heart coverage at 3T. In: Proceedings of the SCMR 23rd Annual Scientific Sessions, Orlando, Florida, USA, 2020.

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Figures

Figure 1. The proposed deep learning-based image reconstruction workflow and the proposed 3D U-Net based image reconstruction network (DESIRE) for spiral first-pass perfusion imaging. The baseline network is shown in (b), which has a number of initial kernels of 16 and a depth of 2. The numbers above each layer denote the number of kernels at each layer, and the corresponding image shape at each layer is also labelled. (c) illustrates the training procedure for SS and SMS MB=2.

Figure 2. Interleaved single-slice prospective spiral perfusion images from a healthy volunteer with 6 slices reconstructed using L1-SPIRiT and the proposed DESIRE image reconstruction network (Ninit=16, D=2). Good image quality was demonstrated using the proposed image reconstruction network. This case reconstructed using DESIRE has an SSIM of 0.956±0.028, a RMSE of 0.011±0.004 and an image quality score of 4.5 (5, excellent; 1, poor).

Figure 3. Retrospective SMS MB=2 spiral perfusion images from a healthy volunteer with 6 slices (a) and prospective SMS MB=2 spiral perfusion images from a healthy volunteer with 6 slices (b) reconstructed using the proposed DESIRE image reconstruction network (Ninit=16, D=2). Good image quality was demonstrated using the proposed image reconstruction network. The case in (a) has an SSIM of 0.921±0.028, a RMSE of 0.012±0.002 and an image quality score of 4 (5, excellent; 1, poor). The case in (b) has an image quality score of 4 (5, excellent; 1, poor).

Figure 4. A patient underwent clinical stress spiral perfusion imaging using the SS acquisition. (a) shows the reconstruction using DESIRE (Ninit=16, D=2). Good image quality was demonstrated with an SSIM of 0.942±0.035, a RMSE of 0.011±0.002. The perfusion defect shows in DESIRE has good agreement with the ground truth. (b) shows the cardiac catherization, where LAD has the complete occlusion. (c) demonstrates the temporal fidelity using the DESIRE has good agreement with the ground truth and the inputs with the preserved temporal fidelity at myocardium circled by the yellow line.

Table 1. Summary of the image reconstruction quality and time for different network structures.

Proc. Intl. Soc. Mag. Reson. Med. 29 (2021)
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